Hobotnica: exploring molecular signature quality

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Molecular Features Sets (MFS) are derived from diverse bioinformatics pipelines.
• A "gold standard" for MFS quality estimation is lacking due to experimental data variability.

Purpose of the Study:

• To propose Hobotnica, a novel approach for estimating the quality of Molecular Features Sets (MFS).
• To assess MFS quality based on their ability to stratify data and distinguish between phenotypes using Distance Matrices (DM).

Main Methods:

• Analyzing inner-sample Distance Matrices (DM) to quantify differences between inner-phenotype and outer-phenotype distances.
• Developing the Hobotnica approach to estimate MFS quality by evaluating data stratification power.
• Assigning significance scores to MFS to compare their quality across contrasting groups.

Main Results:

• The proposed method quantifies the quality of a Distance Matrix (DM) by its discriminative power between phenotypes.
• Hobotnica provides a significance score for MFS, enabling comparative quality assessment.
• This approach allows for the collation and comparison of various molecular signatures.

Conclusions:

• Hobotnica offers a robust method for estimating MFS quality, addressing the lack of a "gold standard".
• The approach facilitates objective comparison of different MFS by quantifying their ability to stratify biological data.
• This work contributes to improving the reliability and interpretability of MFS in biological research.